Exploration of material basis: Chemical composition profile, metabolic profile and pharmacokinetic characteristics in Xingbei Zhike granule.

HEADINGS ETHNOPHARMACOLOGICAL RELEVANCE: Xingbei Zhike granule (XBZK), a widely prescribed Chinese patent medicine, is known for its efficacy in clearing lung qi, relieving cough and reducing phlegm, as well as fever, dry and bitter taste, and irritability. Despite its clinical popularity, comprehensive investigations into its chemical composition, in vivo metabolism, and pharmacokinetic characteristics are limited. AIM OF THE STUDY: This study investigates the chemical composition, in vivo metabolism, and in vivo dynamics of XBZK to clarify its material basis and pharmacokinetic characteristics. MATERIALS AND METHODS: Ultra-high performance liquid chromatography with Orbitrap tandem mass spectrometry (UPLC-Orbitrap-MS) was used to determine the chemical composition and in vivo metabolic profile of XBZK. Additionally, UPLC with triple quadrupole mass spectrometry (UPLC-TQ-MS/MS) was performed to quantify its main components and evaluate its in vivo dynamics in rat plasma. RESULTS: In total, 57 components were identified in XBZK. Furthermore, 40 prototype components and 31 metabolites were detected in various biological matrices of rats, including plasma, tissues, bile, feces, and urine. After administration, the area under the curve (AUC) for ephedrine (Eph), pseudoephedrine (Peph), neotuberostemonine (Neo), amygdalin (Amy), and enoxolone (Eno) exhibited a strong linear relationship with the administered dose (r > 0.9) in all rats. And gender-related differences in the absorption of peiminine (Pmn), peimisine (Pms), and chrysin-7-O-glucuronide (Cog) were notable among rats, with male rats showing a dose-dependent pattern of absorption, while female rats exhibited minimal absorption. CONCLUSIONS: XBZK contains 57 components, primarily composed of flavonoids, alkaloids, and coumarins. The eight main components were rapidly absorbed and eliminated, with some, such as Eph, Peph, Neo, Amy and Eno, following a linear pharmacokinetic pattern. Furthermore, Pmn, Pms and Cog were well absorbed in male rats, showing a dose-dependent behavior.

Identification and quantification of cucurbitacin in watermelon frost using molecular networking integrated with ultra-high-performance liquid chromatography-tandem mass spectrometry.

Watermelon frost, a traditional Chinese medicine produced using watermelon and Glauber's salt, has been widely used for the therapy of oral and throat disorders. Watermelon contains various phytochemical compounds including cucurbitacins and their glycoside derivatives, which have attracted considerable attention because of their medicinal values. However, whether the composition of cucurbitacins existed in watermelon frost was rarely reported. In this study, three cucurbitacins including cucurbitacin B, isocucurbitacin B, and cucurbitacin E were found from watermelon frost extract assisted by ultra-high-performance liquid chromatography-tandem mass spectrometry and molecular networking guided strategy, and the compounds were verified using standard solutions. Furthermore, a quantification method for simultaneously targeted analysis of cucurbitacins was established using ultra-high-performance liquid chromatography-tandem mass spectrometry operating in the multiple reaction monitoring mode. Among them, cucurbitacin B and cucurbitacin E in watermelon frost samples were determined, and the concentrations were 3.78 ± 0.18 and 0.86 ± 0.19 ng/ml, respectively. While isocucurbitacin B was not detected due to the lower content possibly. In conclusion, ultra-high-performance liquid chromatography-tandem mass spectrometry combined with molecular networking is a very useful technique for the rapid identification of unknown cucurbitacin components in watermelon frost.

Rapidly and accurately screening histidine decarboxylase inhibitors from Radix Paeoniae alba using ultrafiltration-high performance liquid chromatography/mass spectrometry combined with enzyme channel blocking and directional enrichment technique.

Histidine Decarboxylase (HDC), an unique enzyme responsible for the synthesis of histamine, which is an important mediator in allergy. Inhibition of HDC activity to decrease histamine production is one way to alleviate allergic symptoms. Traditional Chinese medicines (TCMs) with reported anti-allergy effect is one of important source to search for natural HDC inhibitor. Ultrafiltration combined with high-performance liquid chromatography/mass spectrometry (UF-HPLC/MS) is an effective method for screening HDC inhibitor from TCMs. Nevertheless, false-positive and false-negative results caused by the non-specific binding and the neglection of the trace active compounds are major problems in this method. In this study, an integrated strategy that combined UF-HPLC/MS with enzyme channel blocking (ECB) technique and directional enrichment (DE) technique was developed to seek natural HDC inhibitors from Radix Paeoniae alba (RPA), and at the same time, to reduce false-positive and false-negative results. HDC activity was detected to determine the validity of the screened compounds by RP-HPLC-FD in vitro. Molecular docking was applied to assay the binding affinity and binding sites. As a result, three compounds were screened from low content components of RPA after the DE. Among them, two non-specific compounds were eliminated by ECB, and the specific compound was identified as catechin, which has obvious HDC inhibition activity with IC50 0.52 mM. Furthermore, gallic acid (IC50 1.8 mM) and paeoniflorin (IC50>2 mM) from high content components of RPA were determined having HDC inhibitory activity. In conclusion, the integrated strategy of UF-HPLC/MS combined with ECB and DE technique is an effective mode for rapid and accurate screening and identification of natural HDC inhibitors from TCMs.

Liquiritin inhibits MRGPRX2-mediated pseudo-allergy through the PI3K/AKT and PLCγ signaling pathways.

Liquiritin is a natural flavone with a variety of pharmacological effects derived from the medicinal food homology plant Glycyrrhiza uralensis Fisch. As a kind of lethal allergic reactions, pseudo-allergic reactions (PARs) arise from the Mas-related G protein coupled receptor X2 (MRGPRX2)-triggered fast degranulation of mast cells (MCs). In the current work, the anti-pseudo-allergy action and potential mechanisms of liquiritin were explored in vivo and in vitro. Liquiritin suppressed the calcium influx and degranulation elicited by Compound 48/80 (C48/80) in mouse peritoneal mast cells (MPMCs). In mice, liquiritin also inhibited the C48/80-elicited hind paw extravasation, as well as the elevations in TNF-α and histamine levels. Molecular docking combined with detection of HEK293T cells expressing human MRGPRX2 showed that liquiritin was a potential MRGPRX2 antagonist and inhibited PARs through the PI3K/AKT and PLCγ signaling pathways downstream of MRGPRX2. The present work opens a new avenue for the PARs management.

Anti-pseudo-allergic components in licorice extract inhibit mast cell degranulation and calcium influx.

Pseudo-allergic reactions (PARs) widely occur upon application of drugs or functional foods. Anti-pseudo-allergic ingredients from natural products have attracted much attention. This study aimed to investigate anti-pseudo-allergic compounds in licorice. The anti-pseudo-allergic effect of licorice extract was evaluated in rat basophilic leukemia 2H3 (RBL-2H3) cells. Anti-pseudo-allergic compounds were screened by using RBL-2H3 cell extraction and the effects of target components were verified further in RBL-2H3 cells, mouse peritoneal mast cells (MPMCs) and mice. Molecular docking and human MRGPRX2-expressing HEK293T cells (MRGPRX2-HEK293T cells) extraction were performed to determine the potential ligands of MAS-related G protein-coupled receptor-X2 (MRGPRX2), a pivotal target for PARs. Glycyrrhizic acid (GA) and licorice chalcone A (LA) were screened and shown to inhibit Compound48/80-induced degranulation and calcium influx in RBL-2H3 cells. GA and LA also inhibited degranulation in MPMCs and increase of histamine and TNF-α in mice. LA could bind to MRGPRX2, as determined by molecular docking and MRGPRX2-HEK293T cell extraction. Our study provides a strong rationale for using GA and LA as novel treatment options for PARs. LA is a potential ligand of MRGPRX2.

An integrated strategy for the identification and screening of anti-allergy components from natural products based on calcium fluctuations and cell extraction coupled with HPLC-Q-TOF-MS.

Allergic diseases are a significant public health problem worldwide. Traditional Chinese medicines (TCMs) with reported anti-allergy effects may be important sources for the development of new anti-allergy drugs. Thus, establishing an analytical method that can simultaneously identify and screen anti-allergic compounds in TCMs is important. The increased concentrations of intracellular calcium ions resulting in mast cell degranulation releasing active mediators play a key role in allergic diseases, which can be used as a potential index to identify anti-allergic herbs and compounds. In this study, we provide a new strategy that was applied to screening natural anti-allergic compounds based on fluorescence calcium ion (Ca2+) fluctuation integrated with cell extract and high-performance liquid chromatography-mass spectrometry (HPLC-MS). A low-cost, convenient fluorescence detection Ca2+ signaling method was established and successfully applied to identify three herbs. Then, the method was integrated with biospecific cell fishing and HPLC-MS to screen potential active components that have the effect of stabilizing the cell membrane of rat basophilic leukemia granulocytes (RBL-2H3). Seven components, namely, albiflorin and paeoniflorin from Radix Paeoniae Alba, ononin and formononetin from Radix Astragali, cimifugin, 4'-O-ß-D-glucosyl-5-O-methylvisamminol, and prim-O-glucosylcimifugin from Radix Saposhnikoviae were fished. These seven compounds have the effect of inhibiting cell Ca2+ influx. 4'-O-ß-D-Glucosyl-5-O-methylvisamminol, prim-O-glucosylcimifugin, paeoniflorin, ononin, and formononetin significantly inhibit the release of ß-hexosaminidase, which is equivalent to the positive drug. In conclusion, the integrated strategy of fluorescence detection calcium ion kinetic method binding with biospecific cell fishing was an effective mode to identify and screen natural anti-allergic compounds.

Inhibitory effects of four anthraquinones on tyrosinase activity: Insight from spectroscopic analysis and molecular docking.

In this study, the inhibitory effects of four anthraquinones including chrysophanol, emodin, physcione and rhein on tyrosinase were investigated by enzyme inhibition assay. The results indicated that all of anthraquinones could significantly inhibit the activity of tyrosinase in a competitive manner. To gain insight into the inhibitory mechanism of anthraquinones on tyrosinase, spectroscopic analysis combined with molecular docking studies were performed. Fluorescence results showed that anthraquinones interacted with tyrosinase by static quenching in a molecular ratio of 1:1. Circular dichroism and molecular docking suggested that anthraquinones could not chelate directly the copper ions but they could bind to amino acid residues in the active site of tyrosinase via electrostatic forces and hydrophobic interactions, as well as hydrogen bonds, and the binding processes resulted in the conformational changes of tyrosinase and prevented the substrate (L-DOPA) from entering the active site, which led to the decrease of tyrosinase activity. Our study in this paper provides a scientific basis for revealing the inhibition of tyrosinase activity by anthraquinone compounds. As a natural inhibitor of tyrosinase, anthraquinones can be used as a potential agent to reduce enzymatic browning reactions, such as food browning and melanization of skin.

Investigation on the binding of aloe-emodin with tyrosinase by spectral analysis and molecular docking.

In this paper, the inhibitory kinetics of aloe-emodin on the activity of tyrosinase and the inhibitory mechanism have been investigated by using spectroscopic and molecular docking techniques. The results showed that aloe-emodin inhibited tyrosinase activity in a competitive manner. The binding constants, number of binding sites and thermodynamic parameters obtained at different temperature suggested that aloe-emodin spontaneously binds to tyrosinase at one binding site, mainly via electrostatic forces. Analysis by UV-vis absorption (UV), circular dichroism (CD) and molecular docking indicated that aloe-emodin bound directly into the catalytic cavity and that binding of aloe-emodin to tyrosinase induced conformational changes of the enzyme and blocked the catalytic center of the enzyme preventing binding of the substrate, which caused the inhibition of the tyrosinase activity.

Statistical evaluation of adding multiple risk factors improves Framingham stroke risk score.

BACKGROUND: Framingham Stroke Risk Score (FSRS) is the most well-regarded risk appraisal tools for evaluating an individual's absolute risk on stroke onset. However, several widely accepted risk factors for stroke were not included in the original Framingham model. This study proposed a new model which combines an existing risk models with new risk factors using synthesis analysis, and applied it to the longitudinal Atherosclerosis Risk in Communities (ARIC) data set. METHODS: Risk factors in original prediction models and new risk factors in proposed model had been discussed. Three measures, like discrimination, calibration and reclassification, were used to evaluate the performance of the original Framingham model and new risk prediction model. RESULTS: Modified C-statistics, Hosmer-Lemeshow Test and classless NRI, class NRI were the statistical indices which, respectively, denoted the performance of discrimination, calibration and reclassification for evaluating the newly developed risk prediction model on stroke onset. It showed that the NEW-STROKE (new stroke risk score prediction model) model had higher modified C-statistics, smaller Hosmer-Lemeshow chi-square values after recalibration than original FSRS model, and the classless NRI and class NRI of the NEW-STROKE model over the original FSRS model were all significantly positive in overall group. CONCLUSION: The NEW-STROKE integrated with seven literature-derived risk factors outperformed the original FSRS model in predicting the risk score of stroke. It illustrated that seven literature-derived risk factors contributed significantly to stroke risk prediction.

Studies on the binding of pepsin with three pyrethroid insecticides by multi-spectroscopic approaches and molecular docking.

In this study, the molecular interactions between pepsin and three pyrethroid insecticides, including fenvalerate, cyhalothrin and deltamethrin, were investigated by multi-spectroscopic and molecular docking methods under mimic physiological pH conditions. The results indicated that all of these insecticides could interact with pepsin to form insecticide-pepsin complexes. The binding constants, number of binding sites and thermodynamic parameters measured at different temperatures indicated that these three pyrethroid insecticides could spontaneously bind with pepsin mainly through electrostatic forces and hydrophobic interactions with one binding site. According to the theory of Föster's non-radioactive energy transfer, the distance (r) between pepsin and three pyrethroid insecticides were all found to be less than 7 nm, which implied that the energy transfer occurred between pepsin and these insecticides, leading to the quenching of pepsin fluorescence. Synchronous and three-dimensional fluorescence, CD spectra and molecular docking results indicated that all tested pyrethroid insecticides bound directly into the enzyme cavity site and the binding of insecticides into the cavity influenced the microenvironment of the pepsin activity site which resulted in the extension of peptide strands of pepsin with loss of α-helix structures.Copyright © 2016 John Wiley & Sons, Ltd.

Adding multiple risk factors improves Framingham coronary heart disease risk scores.

PURPOSE: Since the introduction of the Framingham Risk Score (FRS), numerous versions of coronary heart disease (CHD) prediction models have claimed improvement over the FRS. Tzoulaki et al challenged the validity of these claims by illustrating methodology deficiencies among the studies. However, the question remains: Is it possible to create a new CHD model that is better than FRS while overcoming the noted deficiencies? To address this, a new CHD prediction model was developed by integrating additional risk factors, using a novel modeling process. METHODS: Using the National Health Nutritional Examination Survey III data set with CHD-specific mortality outcomes and the Atherosclerosis Risk in Communities data set with CHD incidence outcomes, two FRSs (FRSv1 from 1998 and FRSv2 from National Cholesterol Education Program Adult Treatment Panel III), along with an additional risk score in which the high density lipoprotein (HDL) component of FRSv1 was ignored (FRSHDL), were compared with a new CHD model (NEW-CHD). This new model contains seven elements: the original Framingham equation, FRSv1, and six additional risk factors. Discrimination, calibration, and reclassification improvements all were assessed among models. RESULTS: Discrimination was improved for NEW-CHD in both cohorts when compared with FRSv1 and FRSv2 (P<0.05) and was similar in magnitude to the improvement of FRSv1 over FRSHDL. NEW-CHD had a similar calibration to FRSv2 and was improved over FRSv1. Net reclassification for NEW-CHD was substantially improved over both FRSv1 and FRSv2, for both cohorts, and was similar in magnitude to the improvement of FRSv1 over FRSHDL. CONCLUSION: While overcoming several methodology deficiencies reported by earlier authors, the NEW-CHD model improved CHD risk assessment when compared with the FRSs, comparable to the improvement of adding HDL to the FRS.

Underwriting mortality risk using cognitive function tests.

OBJECTIVE: Evaluate the association of selected cognitive function assessments, including two memory tests and two mental status tests, with all-cause mortality. METHOD: Associations between the selected cognitive function tests and mortality were assessed in a longitudinal dataset. The Health and Retirement Study (HRS) includes 27,648 individuals, most of whom were 65 years of age and older; study participants were followed for an average of 8.9 years, and over this interval, 8268 deaths occurred. The association of 4 cognitive function test scores at entry into the study and the observed rates of mortality were evaluated using both a traditional (actuarial) actual vs expected mortality method and a Cox proportional hazard model adjusted for age, gender, smoking, and other covariates. RESULTS: Each cognitive function test was shown to be independently associated with mortality after adjustment for covariates. Further, individual test scores, fit using a continuous model, were shown to be correlated with mortality outcomes. The associations appear to be stronger at a younger age when only age, gender, and smoking were adjusted, but such effect modifications were no longer statistically significant after additional covariates were adjusted. The associations did not appear to be attenuated in a pre-defined "healthy" subgroup, suggesting that the result could be extrapolated to applicants who would qualify for life insurance. CONCLUSIONS: Cognitive function, as measured by 4 simple screening tests, was shown to be significantly and independently associated with all-cause mortality in a longitudinal dataset of individuals, the majority of whom were 65 years of age and older.

A new synthesis analysis method for building logistic regression prediction models.

Synthesis analysis refers to a statistical method that integrates multiple univariate regression models and the correlation between each pair of predictors into a single multivariate regression model. The practical application of such a method could be developing a multivariate disease prediction model where a dataset containing the disease outcome and every predictor of interest is not available. In this study, we propose a new version of synthesis analysis that is specific to binary outcomes. We show that our proposed method possesses desirable statistical properties. We also conduct a simulation study to assess the robustness of the proposed method and compare it to a competing method.

Associations between selected laboratory tests and all-cause mortality.

OBJECTIVE: Evaluate the association of selected laboratory tests including liver function, kidney function, bilirubin, and albumin, with all-cause mortality METHOD: Associations between several laboratory tests and mortality were assessed in two longitudinal datasets: (1) the Third National Health and Nutritional Examination Survey (NHANES III) with 4610 deaths during the 240,428 person-year follow up: and (2) a life insurance dataset containing historical life insurance policies issued as standard and better risk class, with 837 death claims generated during the approximate 1.4 million person-years of follow up. A Cox proportional hazards model was used to compute the hazard ratio of each selected laboratory test while adjusting for age, gender, and other health conditions. Separate analyses were conducted for laboratory results within and beyond the respective normal clinical range. RESULTS: When outside the normal clinical range, the results of several selected laboratory tests were associated with higher mortality, as indicated by a hazard ratio greater than 1. Comparisons of hazard ratios when laboratory results were within the normal range demonstrated that blood albumin and blood urea nitrogen (BUN) were both negatively associated with mortality and alkaline phosphatase (AP) was positively associated with mortality. The associations were shown to be independent and were consistent in two datasets. CONCLUSIONS: Although abnormal laboratory results are significant predictors of higher mortality, when results fall within the normal clinical range, only three tests--albumin, AP, and BUN--provided mortality differentiation. These findings support the utilization of the actual, continuous value from albumin, AP, and BUN tests to evaluate mortality during underwriting, even when these results are categorized as clinically normal. Furthermore, the results provide an insightful perspective for evaluating the utility of recently developed, laboratory test-based underwriting toools.

Relationship between selected social factors and all-cause mortality.

OBJECTIVE: Evaluate the associations between several simple-to-measure social factors and all-cause mortality to determine whether selected social factors contribute useful mortality information. METHOD: Using the Third National Health and Nutritional Examination Survey (NHANES III) and the current NHANES III Linked Mortality File datasets, associations were evaluated among 18,460 survey participants at least 20 years of age, with 5408 deaths occurring during the 280,183 person-year follow-up. Selected social factors, including education level, current employment status, and frequency of interpersonal contact with friends/relatives, were analyzed using a Cox proportional hazard model, and the impact of the selected social factors on mortality was expressed as a hazard ratio. Associations were modeled adjusting for age and gender only and also in a multivariate regression analysis; furthermore, associations were evaluated when stratified by duration of follow up. RESULTS: In a multivariate Cox model, independent hazard ratios for higher education, being employed, being married, frequent phone conversations with friends, frequent visits with friends or relatives, frequent church attendance, and participation in a social group ranged between 0.56 and 0.99. All corresponding 95% confidence intervals exclude 1. Many of the associations between social factors and mortality were stronger at shorter follow-up durations. CONCLUSION: Several easy-to-measure social factors were shown to be significantly and independently associated with all-cause mortality.

Synthesis analysis of regression models with a continuous outcome.

To estimate the multivariate regression model from multiple individual studies, it would be challenging to obtain results if the input from individual studies only provide univariate or incomplete multivariate regression information. Samsa et al. (J. Biomed. Biotechnol. 2005; 2:113-123) proposed a simple method to combine coefficients from univariate linear regression models into a multivariate linear regression model, a method known as synthesis analysis. However, the validity of this method relies on the normality assumption of the data, and it does not provide variance estimates. In this paper we propose a new synthesis method that improves on the existing synthesis method by eliminating the normality assumption, reducing bias, and allowing for the variance estimation of the estimated parameters.

Building prediction models for coronary heart disease by synthesizing multiple longitudinal research findings.

BACKGROUND: No methodology is currently available to allow the combining of individual risk factor information derived from different longitudinal studies for a chronic disease in a multivariate fashion. This paper introduces such a methodology, named Synthesis Analysis, which is essentially a multivariate meta-analytic technique. DESIGN: The construction and validation of statistical models using available data sets. METHODS AND RESULTS: Two analyses are presented. (1) With the same data, Synthesis Analysis produced a similar prediction model to the conventional regression approach when using the same risk variables. Synthesis Analysis produced better prediction models when additional risk variables were added. (2) A four-variable empirical logistic model for death from coronary heart disease was developed with data from the Framingham Heart Study. A synthesized prediction model with five new variables added to this empirical model was developed using Synthesis Analysis and literature information. This model was then compared with the four-variable empirical model using the first National Health and Nutrition Examination Survey (NHANES I) Epidemiologic Follow-up Study data set. The synthesized model had significantly improved predictive power (chi = 43.8, P<0.00001). CONCLUSIONS: Synthesis Analysis provides a new means of developing complex disease predictive models from the medical literature.

Combining information from multiple data sources to create multivariable risk models: illustration and preliminary assessment of a new method.

A common practice of metanalysis is combining the results of numerous studies on the effects of a risk factor on a disease outcome. If several of these composite relative risks are estimated from the medical literature for a specific disease, they cannot be combined in a multivariate risk model, as is often done in individual studies, because methods are not available to overcome the issues of risk factor colinearity and heterogeneity of the different cohorts. We propose a solution to these problems for general linear regression of continuous outcomes using a simple example of combining two independent variables from two sources in estimating a joint outcome. We demonstrate that when explicitly modifying the underlying data characteristics (correlation coefficients, standard deviations, and univariate betas) over a wide range, the predicted outcomes remain reasonable estimates of empirically derived outcomes (gold standard). This method shows the most promise in situations where the primary interest is in generating predicted values as when identifying a high-risk group of individuals. The resulting partial regression coefficients are less robust than the predicted values.

Accuracy of prediction models in the context of disease management.

There has been a significantly increased interest in the adoption of prediction modeling by many disease and case management programs to risk stratify members in order to optimize the utilization of available clinical resources. Before adopting any prediction model, it is critical to understand how to evaluate the model's accuracy. This paper explains the basic concepts of prediction accuracy, the relevant parameters, their drawbacks, and their interpretations. It also introduces a new accuracy parameter termed "cost concentration," which indicates the model accuracy more explicitly in the context of disease management.

The differences between claim-based health risk adjustment models and cost prediction models.

There has been a significant increase in interest in using risk assessment tools with administrative claims data for provider profiling, provider payment, underwriting and disease/case management. The tools can be classified into two types: risk adjustment models and cost prediction models. The differences between the two models have not been well recognized. This paper explains the differences in terms of the objectives, the applications, and the accuracy of evaluations.